Estimating overall persistence and long-range transport potential of persistent organic pollutants: a comparison of seven multimedia mass balance models and atmospheric transport models

Abstract

Two different approaches to modeling the environmental fate of organic chemicals have been developed in recent years. The first approach is applied in multimedia box models, calculating average concentrations in homogeneous boxes which represent the different environmental media, based on intermedia partitioning, transport, and degradation processes. In the second approach, used in atmospheric transport models, the spatially and temporally variable atmospheric dynamics form the basis for calculating the environmental distribution of chemicals, from which also exchange processes to other environmental media are modeled. The main goal of the present study was to investigate if the multimedia mass balance models CliMoChem, SimpleBox, EVn-BETR, G-CIEMS, OECD Tool and the atmospheric transport models MSCE-POP and ADEPT predict the same rankings of the overall persistence (P ov ) and long-range transport potential (LRTP) of POPs, and to explain differences and similarities between the rankings by the mass distributions and inter-compartment mass flows. The study was performed for a group of 14 reference chemicals. For P ov , the models yield consistent results, owing to the large influence of phase partitioning parameters and degradation rate constants, which are used similarly by all models. Concerning LRTP, there are larger differences between the models than for P ov , due to different LRTP calculation methods and spatial model resolutions. Between atmospheric transport models and multimedia fate models, no large differences in mass distributions and inter-compartment flows can be recognized. Deviations in mass flows are mainly caused by the geometrical design of the models.

Understanding Quantitative Structure–property Relationships Uncertainty in Environmental Fate Modeling

“…Several models are available to assess the fate of chemicals. In contrast to P OV , LRTP has been shown to be sensitive to the choice of model [31][32][33]. Differences in LRTP may result from differences in calculation methods and spatial model resolutions.…”

“…Chem. 32,2013 Propagation of uncertainty Uncertainty in QSPR-predicted input parameters was propagated by Monte Carlo simulations, with 10,000 simulation runs performed in Microsoft Excel for SimpleBox using the simulation software Crystal Ball 2000, version 5.2.2 (Decisioneering). Two separate simulations were performed with two different scenarios, resulting in a pair of cumulative probability distributions as assessment outcomes that produce a probability box (p-box; Fig.…”

“…Chem. 32,2013 Note that the contribution of uncertainty in QSPR-predicted parameters, such as the contribution of the hydroxyl radical reaction rate to P OV of BDE-99 without photolysis, was negative ( Fig. 4), suggesting that setting this input parameter to a point estimate results in a wider overall uncertainty.…”

Spatially explicit multimedia fate models for pollutants in Europe: State of the art and perspectives

“…Things are complicated by the importance that dispersión takes on advection in these cases. The representative transporting currents estimated in the above quoted models are likely to reproduce the correct order of magnitude of advection and dispersión removal rates, but cannot be used to describe spatial patterns arising from these mechanisms, such as contamination plumes from a source of emissions (see, for instance, the comparison between box models and atmospheric transport models presented in Hollander et al, 2008).…”

Long‐range transport of organic chemicals in the environment

“…Wania [24] determined the ACP for a set of hypothetical chemicals with a broad range of chemical properties and suggested a chemical classification in terms of ''single hoppers,'' ''multiple hoppers,'' ''swimmers,'' and ''flyers,'' according to the way in which the chemicals are transported to the Arctic (see also next section). Another target-oriented metric is the fraction of a chemical that remains in a remote region, which is calculated as the cumulative net mass flow of a chemical into a target region that occurs in a certain period of time divided by the amount of the chemical that was released during the same period of time [26].…”